Regions of Europe
From Statistics Explained
- Data from September 2009, most recent data: Further Eurostat information, Main tables and Database.
European regional policy is designed to further economic and social cohesion, by reducing the gap in development between regions and among Member States of the European Union (EU). Regional policy helps finance concrete projects for regions and towns, stimulating growth and competitiveness; as such, it is in line with the priorities set by the EU for growth and employment (the Lisbon Strategy).
Main statistical findings
The maps presented in this article illustrate the diversity of the EU’s 271 NUTS level 2 regions and show that large variations may exist for many economic and social characteristics, not only across the Member States, but also within countries; where available, information has also been included for Candidate countries and for EFTA countries.
Economic trends across regions
GDP per inhabitant in the EU-27 averaged PPS 23 600 for 2006, while among the regions it ranged from a high of PPS 79 400 per inhabitant in Inner London to PPS 5 800 per inhabitant for Nord-Est (Romania); the factor between the two ends of the distribution was therefore 13.7:1. The next highest levels of GDP per inhabitant were recorded for Luxembourg (PPS 63 100) and Brussel/Bruxelles (PPS 55 100), while Hamburg (PPS 47 200) was the only other region to register a level that was at least twice as high as the EU-27 average. Among the 20 regions with the highest levels of GDP per inhabitant, Praha (the Czech Republic) and Bratislavský kraj (Slovakia) were the only regions from the Member States that joined the EU since 2004, ranked in 12th and 19th place respectively. The nine ‘poorest’ regions (using this measure) were all in Bulgaria and Romania, with a number of Polish, Romanian and Hungarian regions making up the remainder of the bottom 20 in the ranking.
An analysis across those countries where there are several NUTS level 2 regions shows that Berlin, Rome, Amsterdam and Helsinki were the only capital city regions in 2006 not to record the highest levels of national GDP per inhabitant; Hamburg, the Provincia Autonoma Bolzano/Bozen and Lombardia (which contains Milan), Groningen (north east Holland), and Åland (south west Finland) reported higher levels of GDP per inhabitant than regions containing the capital city.
More generally, GDP per inhabitant tended to be relatively high in northern Belgium, southern Germany, northern Italy, the south of the United Kingdom, Ireland, Luxembourg, the Netherlands, Austria and Scandinavia, as well as the capital city regions of Prague, Madrid and Paris. GDP per inhabitant was relatively low in many of western regions of the Iberian Peninsula, southern Italy, Greece (aside from Athens) and eastern Germany, as well as in most of the Member States that joined the EU since 2004.
There were substantial regional differences within Member States as regards the distribution of GDP per inhabitant. The ratio between the highest and lowest values stood at a factor of 4.3:1 in the United Kingdom between Inner London and West Wales and the Valleys, while in France the ratio was 3.5:1 between the Île de France (which includes Paris) and Guyane (one of the French overseas departments). At the other end of the scale, the most ‘equitable’ distributions of GDP per inhabitant were recorded in Denmark, Ireland, Spain, the Netherlands, Portugal, Slovenia and Sweden, where the ratio between the highest and lowest regional values never rose above 2:1.
Data for GDP per inhabitant should be interpreted with care as this ratio is influenced by the number of commuters working in one region but living in another. Indeed, the relatively high levels of GDP per inhabitant within Inner London, Luxembourg and Brussel/Bruxelles (the three regions with the highest GDP per inhabitant) can, at least in part, be explained by a large daily influx of commuters from neighbouring regions or, in the case of Luxembourg, neighbouring countries. This effect can vary considerably and may reflect not only the propensity to commute or the distances that people are prepared to commute, but also the way NUTS level 2 regions are delineated and, in particular, how far the suburbs and surrounding areas of cities are included within the same NUTS region. Conversely, the counter-effect of commuters working in a neighbouring region tends to result in the GDP per inhabitant of ‘commuter belts’ or ‘dormitory’ regions being lower – examples include Lüneburg near Hamburg, Flevoland near Amsterdam, and several regions in Belgium (as Belgian commuters travel not only to Brussel/Bruxelles but also to Luxembourg).
When comparing the regional distribution of disposable income per inhabitant with that of GDP per inhabitant there are considerable differences, as income measures are not affected by commuter flows. A comparison between GDP per inhabitant for Inner London and for Surrey, East and West Sussex (a popular commuter belt to the south of London) shows that GDP per inhabitant was 2.69 times as high in Inner London. However, in terms of disposable income the difference between the two regions was much closer, as the disposable income figures reflect where each of these commuters lives (principally in areas around the capital). As such, disposable income in Inner London was PPS 25 403 in 2006, only 1.17 times as high as the figure for Surrey, East and West Sussex.
Inner London recorded the highest level of disposable income across all EU-27 NUTS level 2 regions in 2006. Of the nine regions in the EU-27 where disposable income per inhabitant was above the threshold of PPS 20 000 in 2006, five (including Inner London) were in the south east of the United Kingdom, three in Germany and one in France. Comparing the highest and lowest levels of disposable income per inhabitant across all EU-27 regions, incomes were higher in Inner London by a factor of 7.0:1 when compared with Nord-Est (Romania); this ratio was approximately half as pronounced as that recorded in terms of GDP per inhabitant for the same two regions (13.7:1).
Regional disparities (based on a comparison of the highest to the lowest levels of disposable income) within the same country were considerable in Greece and Romania; where disposable income per inhabitant in the two capital city regions was more than double that recorded in the region with the lowest levels of disposable income – Ionia Nisia (a group of islands off the west coast of Greece, including Corfu) or Nord-Est (Romania). Regional disparities were also generally high across Italy, Hungary, Slovakia and the United Kingdom, whereas the lowest disparities (using this measure) were recorded in Austria and Slovenia, followed by Ireland, the Netherlands, Finland and Sweden.
Demographic trends across regions and cities
Population density is measured in terms of the average number of inhabitants per square kilometre (km²) of land area; this ratio stood at 122 inhabitants in 2007 for the EU-27. Information broken down by NUTS level 2 regions is generally available for 2007 and this shows that capital city regions are among the most densely populated regions in Europe, for example, Inner London and Outer London (United Kingdom), Brussel/Bruxelles (Belgium), Wien (Austria), Berlin (Germany), Praha (the Czech Republic), Istanbul (Turkey), Bucureşti-Ilfov (Romania) and Attiki (Greece). Each of these capital city regions had a population density above 1 000 inhabitants per km², as did the following non-capital city regions: West Midlands, Merseyside, Greater Manchester and West Yorkshire (United Kingdom), Hamburg and Bremen (Germany), the autonomous regions of Ceuta and Melilla (Spain), Malta (the whole island is defined as one NUTS level 2 region), and Zuid-Holland (Netherlands).
The least densely populated regions in 2007 were Guyane (France), Iceland (the whole country is defined as one NUTS level 2 region), and Övre Norrland (Sweden), all three with an average of three inhabitants per km². The next least densely populated regions, registering less than 20 inhabitants per km², were all in Sweden, Finland, the United Kingdom and Norway, while several regions across Spain (Aragón, Castilla-la Mancha, Castilla y León and Extremadura) and one in southern Portugal (Alentejo) were the only other EU-27 regions to record a population density of less than 30 inhabitants per km².
Around three quarters of the EU-27’s population lives in cities or towns with more than 5 000 inhabitants. Information from the urban audit data collection shows that 26 of the more than 350 cities surveyed in 2007 had a population in excess of one million inhabitants (21 in the EU-27 and five in Turkey).
Istanbul was the largest of the urban audit cities, with a population of 9 million inhabitants (about the same number as the total population of Sweden), followed by London and Paris (7.4 million and 6.2 million respectively), Berlin, Ankara and Madrid (all in the range of 3 to 3.5 million inhabitants). Most of the agglomerations with more than 1.5 million inhabitants were capital cities, although Hamburg in Germany, Barcelona in Spain, and Istanbul and Izmir in Turkey were exceptions to this rule.
While Guyane (France) reported the lowest population density among EU-27 regions, it also reported the highest population growth (3.7 % per annum during the period 2002 to 2006). Of the 12 regions that reported population growth in excess of 2 % per annum during the period considered (generally 2003 to 2007), eight were in Spain, principally in the islands, easterly coastal regions, and around Madrid (the Comunidad de Madrid and Castilla-la Mancha which is south east of Madrid); the three other regions included both Irish regions (there are only two NUTS level 2 regions in Ireland) and another island region, namely, Corse (France).
Just over a quarter (25.8 %) of the 287 regions for which data are available reported a decline in their populations during the period 2003 to 2007. Of these, three regions recorded reductions in excess of 1 % per annum; two in eastern Germany (Chemnitz and Sachsen-Anhalt) and one in north west Bulgaria (Severozapaden).
Population ageing is likely to have a significant impact on a range of social and economic issues in the coming years, including education, the labour market, healthcare, social security and pension provisions. Relatively low fertility levels, combined with extended longevity have led to the demographic ageing of the EU-27 population, with older generations accounting for an increasing proportion of the total population, in contrast to the diminishing share of those of a working age.
Rural, agricultural areas of Greece, France, Italy and Portugal, as well as eastern regions of Germany (such as Chemnitz, Dresden or Sachsen-Anhalt) tended to record the highest old-age dependency ratios (the number of elderly persons aged 65 and over relative to the number of persons of working age (15 to 64)). The relatively high proportion of elderly persons is often a reflection of younger age groups finding it necessary to leave the region in their quest to find work. The highest old-age dependency ratio was recorded in Liguria (Italy) at 43.2 % in 2008, while five other Italian regions Umbria, Toscana, Friuli-Venezia Giulia, Emilia-Romagna and Piemonte each reported rates of at least 35 %.
At the other end of the range, all 25 Turkish regions reported very low old-age dependency ratios – the lowest (5.9 %) was recorded for Van (eastern Turkey). There were also relatively low old-age dependency ratios recorded in Southern and Eastern Ireland, the French overseas departments of Guyane and Réunion, Flevoland (the Netherlands), the Polish regions of Lubuskie and Warmińsko-Mazurskie, Východné Slovensko (Slovakia), Inner London (United Kingdom) and the former Yugoslav Republic of Macedonia. These ratios may be associated with a range of influences, such as: relatively high fertility rates (boosting the share of the young in the total population); relatively low life expectancy (resulting in fewer persons aged 65 and over), or inward migratory patterns (whereby mainly younger persons move to a region in search of work, thereby lowering the relative share of older generations).
Labour market trends across regions
The Lisbon Strategy set an objective of attaining an overall employment rate of 70 % by 2010; the EU rate stood at 65.9 % in 2008. A regional breakdown of this headline figure shows that 94 of the 271 NUTS level 2 regions (for which data are available) reported employment rates in excess of the Lisbon target. The range between the highest and the lowest regional employment rates in 2008 was considerable, as the high of 82.5 % in Åland (Finland) was almost double the figure recorded for Campania (Italy), 42.5 %.
A cluster of regions in southern Germany and Austria recorded relatively high employment rates, as did a number of northern European regions in Denmark, the Netherlands, Finland, Sweden and the United Kingdom. In contrast, generally low regional employment rates were often registered in many parts of southern Spain and southern Italy, as well as in eastern Europe. More specifically, there were 12 regions in the EU that reported employment rates below the threshold of 50 % in 2008; five in southern Italy, the four French overseas departments, two in eastern Hungary, and the autonomous region of Melilla (Spain).
The Lisbon Strategy also set an objective for attaining a 50 % old-age (those aged 55 to 64 years) employment rate by 2010; the overall EU rate stood at 45.6 % in 2008 (55.0 % for men and 36.9 % for women). The differences between regions and between men and women may often result from socio-economic and cultural forces – for example, the propensity of older generations to help look after children in their extended family, or differences in attitudes towards older persons continuing to work. Scandinavian countries, the Baltic Member States, the Netherlands and the United Kingdom recorded some of the highest employment rates among older workers. At the other end of the range, Belgium, France, Italy, Luxembourg, Hungary, Malta, Poland and Slovenia recorded some of the lowest rates.
The old-age employment rate ranged from a high of 75.9 % in Åland (Finland) to a low of 21.9 % in Dél-Dunántúl (Hungary) in 2008. Some 113 of the 271 regions for which data are available within the EU recorded an old-age employment rate that was in excess of the Lisbon target of 50 %. Of these, there were 31 with an old-age employment rate of more than 60 %, four of which – Åland (Finland), Småland med öarna, Stockholm and Västsverige (all Sweden) – recorded rates in excess of 70 %.
In the EU-27, some 38 of the 271 NUTS level 2 regions for which data are available for 2008 recorded double-digit unemployment rates; these were mainly located in eastern regions of Germany, the south of Spain, the French overseas departments, the south of Italy, as well as several regions in Belgium, Hungary and Slovakia. In contrast, the lowest levels of unemployment were recorded across the Netherlands and Austria, in the north of Belgium, in and around Praha (the Czech Republic), in the north of Italy and in the south of the United Kingdom. Eight of the 20 regions that recorded the lowest unemployment rates (3.0 % or less) in 2008 were Dutch, while there were three regions each from Austria and the United Kingdom, two from the Czech Republic, and one each from Belgium, Bulgaria, Italy and Finland.
Regional disparities within and between countries
The majority of funds for economic and social cohesion policy are attributed to those regions where GDP per inhabitant lies below the threshold of 75 % of the EU average. Twelve of the Member States (Belgium, Denmark, Germany, Ireland, Cyprus, Luxembourg, Malta, the Netherlands, Austria, Finland, Sweden and the United Kingdom) reported that none of their regions qualified for such funds, on the basis of the latest GDP per inhabitant figures for 2006.
The success of any regional policies designed to further economic and social cohesion can be analysed through studying regional disparities over time – for example, by measuring the convergence of regional GDP per inhabitant, regional employment rates or regional unemployment rates.
The dispersion of GDP per inhabitant across NUTS level 2 regions can be calculated in terms of a coefficient. When considering all EU-27 regions, this coefficient fell in successive years from 31.8 % in 2001 to 28.9 % by 2006. However, a number of Member States reported that disparities in regional GDP per inhabitant increased; this was notably the case between 2001 and 2006 in Bulgaria, Greece, Hungary, Romania and Slovakia.
The dispersion of employment rates (measured using the coefficient of variation) across NUTS level 2 regions stood at 11.1 % for the EU-27 in 2007. This marked a decrease of 1.8 percentage points when compared with the same ratio for 2003, having posted no change in the previous four-year period (1999 to 2003). Over the four-year period 2003 to 2007, there was a reduction in the regional dispersion of employment rates across the Czech Republic, Germany, Spain, Poland and Sweden (by more than 1 percentage point), while there was an increase of more than 1 percentage point across Hungarian and Romanian regions.
The largest disparities in employment rates in 2007 were observed among Italian and Hungarian regions. In the former, employment rates reached a high of 70.5 % for the northern Provincia Autonoma of Bolzano/Bozen, while the lowest rate was recorded for the southern region of Campania (42.5 %). Employment rates across the Netherlands and Sweden were, in contrast, characterized by a high degree of uniformity; maximum and minimum rates were 79.8 % for Utrecht and 73.0 % for Groningen among Dutch regions, and 77.4 % for Småland med öarna and 72.1 % for Övre Norrland among Swedish regions.
An alternative measure for measuring the performance of different regions within the same Member State is to compare regional employment rates with the national average. ‘Underperformance’ may be identified by comparing regional values against a particular threshold (for example, 90% of the national figure). Using this measure, at NUTS level 2, there were 26 underperforming regions out of a total of 265 for which data are available for 2008. Of these, France and Italy each accounted for six regions, Spain for five, Hungary for three, Belgium and the United Kingdom for two, and Germany and Finland for one region.
The dispersion of regional unemployment rates across NUTS level 2 regions in the EU was 44.1% in 2007. As such, there was some degree of convergence when compared with 2003, as this ratio had previously stood at 58.7 %. Female unemployment rates converged at a more rapid pace than male unemployment rates during the period under consideration, although there was generally a higher degree of dispersion among female unemployment rates. During the period 2003 to 2007, regional unemployment rates in Italy and Portugal converged; in contrast, the dispersion of unemployment rates between the regions of Bulgaria, Belgium, Romania and Slovakia widened considerably.
The highest dispersion of unemployment rates in 2007 was observed across the regions of Belgium and Italy (despite the Italian figure being reduced considerably when compared with 2003). In Belgium, the unemployment rate peaked in Brussel/Bruxelles at 15.9 % in 2008, which was almost six times as high as the rate for the Prov. West-Vlaanderen (2.7 %). In a similar vein, the difference between the highest and lowest regional unemployment rates in Italy was also close to a factor of 6:1 between Sicilia (13.8 %) and the Provincia Autonoma of Bolzano/Bozen (2.4 %).
As for the employment rate, a similar measure exists for analysing ‘underperformance’ on the basis of unemployment figures, whereby those regions with unemployment rates that are more than 150 % of the national average are deemed to be underperforming. Using this criteria for NUTS level 2 in 2008, there were 33 out of a total of 264 regions for which data are available that were identified as underperforming, including: seven regions in Germany, six in Italy, four in Spain and in France, two in Belgium, Bulgaria, the Czech Republic, Hungary and the United Kingdom, and one in Greece and Austria.
Data sources and availability
Regional data cover a broad range of statistical areas, for example:
- regional economic accounts;
- demography and migration;
- employment and unemployment;
- education and health;
- agriculture, industry, distributive trades and other services;
- tourism and transport;
- research and development.
The concepts and definitions used for regional statistics are as close as possible to those used for the production of statistics at a national and European level.
The NUTS (nomenclature of territorial units for statistics) is the nomenclature subdividing the territory of the EU into regions at three different levels (NUTS 1, 2 and 3, respectively, from larger to smaller); above that there is the ‘national’ level of the Member States. The NUTS aims to provide a single and coherent territorial breakdown for the compilation of EU regional statistics. The current NUTS version of 2006 subdivides the territory of the EU and its 27 Member States into 97 NUTS 1 regions, 271 NUTS 2 regions and 1 303 NUTS 3 regions. The NUTS is based on Regulation 1059/2003on the establishment of a common classification of territorial units for statistics. An amending Regulation, extending the NUTS to the ten Member States that joined the EU in 2004, was adopted in 2005 and an amendment that extends the NUTS to cover Bulgaria and Romania, was adopted in 2008.
GDP per inhabitant: the economic development of a region is, as a rule, expressed in terms of its gross domestic product (GDP). However, in order to take account of the different absolute sizes of regions, any comparison of economic development should take account of population. GDP per inhabitant should preferably be expressed in terms of a common currency that eliminates differences in price levels between countries. For this purpose, GDP is converted using conversion factors, known as purchasing power parities (PPPs), to an artificial common currency, called a purchasing power standard (PPS). Note that GDP per inhabitant is based on a measure of wealth (the GDP produced in the region) that relates to the ‘place-ofwork’, which is subsequently divided by a ‘place-of-residence’ figure (inhabitants living in the region). This inconsistency can be particularly relevant wherever there are considerable commuter flows – i.e. more or fewer people working in a region than living in it (for example, Inner London, Wien, Hamburg, Praha or Luxembourg). As such, a more balanced picture of a region’s economic situation may be obtained by analysing GDP per inhabitant figures alongside indicators that measure the regional distribution of income.
Disposable income per inhabitant: aside from interregional flows of labour (commuter flows), there are a number of additional factors that can result in the regional distribution of income deviating from the regional distribution of GDP. These include, for example, interregional flows of income from rent, interest or dividends received by the residents of a certain region, but paid by residents of other regions. In contrast to GDP per inhabitant, the disposable income of private households presents the balance remaining after these transactions have been carried out, based on the income received (wages, operating surplus, rent, interest, dividends and social benefits) from which are deducted taxes, social security contributions and other current transfers. The data are derived from household accounts and are (as with the GDP figures) presented in terms of an artificial common currency, a purchasing power consumption standard (PPCS) per inhabitant in order to eliminate differences in price levels between countries.
Population density: the ratio of average population, defined as the number of inhabitants, relative to the size of the territory in square kilometres (km²); the land area concept (excluding inland waters like lakes or rivers) is used wherever available.
Population change: the difference in population between two reference periods (at the beginning of each year) expressed in terms of an average annual growth rate. Population change measures the sum of natural increase (births minus deaths) and net migration (immigration minus emigration).
Old-age dependency ratio: the ratio between the total number of elderly persons of an age when they are generally economically inactive (aged 65 and over) and the number of persons of working age (15 to 64).
The primary source of regional labour market information is the labour force survey (LFS); this is a quarterly household sample survey. The target population is made up of all members of private households aged 15 or over. The data presented refer to annual averages of the quarterly surveys.
Employment rate: employed persons are all persons aged 15 and over (16 and over in Spain and the United Kingdom, 15 to 74 in Denmark, Estonia, Latvia, Hungary, Sweden and Finland) who, during the reference week, worked at least one hour for pay or profit, or were temporarily absent from such work; family workers are included. The employment rate expresses persons employed as a proportion of the total target population.
Old-age employment rate: as above for the employment rate, but based on a target population of those persons aged 55 to 64 years old.
Unemployment rate: unemployed persons comprise those aged 15 to 74 (16 to 74 in Spain and the United Kingdom) who were (all three conditions need to be satisfied simultaneously): without work during the reference week (of the LFS); available for work; and actively seeking work. The latter involves taking specific steps in the four-week period ending with the reference week (of the LFS) to either: contact a public employment office to find work; contact a private temporary work or recruitment agency; apply directly to employers to find work; or finding a job to start within a period of at most three months. The unemployment rate expresses the number of unemployed persons as a proportion of the active population (which comprises all employed and unemployed persons).
One means of quantifying economic and social cohesion is through an analysis of the dispersion of regional indicators – in other words, how evenly an indicator is spread across EU regions, or among the different regions of the same Member State. Such measures of dispersion are presented here for GDP per inhabitant, employment rates and unemployment rates. In order to interpret the results, note that, for example, the dispersion of regional employment rates will be zero if the employment rate of each region is identical, and will rise the larger the differences in employment rates between regions. Given these indicators have been produced at NUTS level 2, they are not applicable for Estonia, Ireland, Cyprus, Latvia, Lithuania, Luxembourg, Malta or Slovenia, as these Member States comprise only one or two regions at this level of detail. The measure of dispersion is generally expressed in terms of the coefficient of variation, which presents the ratio of the weighted standard deviation of the regional measures compared with the overall national rate.
The main goal of the Urban audit data collection is to provide information to assess the quality of life in European towns and cities. The Urban audit provides statistical data for 321 cities across the Member States, as well as for five cities in Croatia, six in Norway, four in Switzerland and 26 in Turkey. These cities were selected in cooperation with the national statistical offices, and are geographically dispersed to ensure a representative sample, meaning that they are not necessarily always the largest cities.
Eurostat collects and publishes information on over 330 indicators relating to the quality of urban life and living standards, including information on: demography, housing, health, crime, the labour market, economic activity, income disparity, local administration, civic involvement, educational qualifications, cultural infrastructure and tourism. All definitions follow as closely as possible definitions employed for national and regional figures; in the event that a different definition is used, data providers are asked to estimate the data in line with the standardised definitions.
Data are collected at a number of different levels, namely: core cities, larger urban zones and sub-city districts (for a smaller subset of indicators). The urban audit defines a city as a legal entity (administrative concept), and delineates the ‘core city’ according to political and administrative boundaries; note that this concept is not always strictly comparable between countries due to the different structures of local government that may exist across countries. As economic activity, health services or air pollution, among others, cross the administrative boundaries of cities, the ‘larger urban zone’ is defined for analytical purposes as the core city and its commuter belt. Each core city is, in turn, divided into a number of ‘subcity districts’, enabling information to be collected on possible disparities within cities.
During the current programming period which covers 2007 to 2013, economic and social cohesion policy across the regions will benefit from EUR 347 410 million. The three main objectives are:
- convergence, under which the poorest Member States and regions (GDP per inhabitant less than 75 % of the EU average) are eligible, accounting for around 82 % of the funds for 2007 to 2013;
- regional competitiveness and employment, accounting for around 16 % of the funds; all regions which are not covered by the convergence objective or transitional assistance are eligible for funding;
- European territorial cooperation, accounting for around 2.5 % of the funds available.
Regional statistics are employed for a range of purposes, including the allocation of structural funds. NUTS, the common classification of territorial units for statistics, is used as an objective base to demarcate regional boundaries and determine geographic eligibility for funds, including:
- the European Regional Development Fund (ERDF) which operates in all Member States and co-finances physical investments and, to a limited extent, training; the fund can intervene in the three objectives of regional policy;
- the European Social Fund (ESF) which aims to make the EU’s workforce and companies better equipped to face global challenges through the promotion of better skills and job prospects;
- the Cohesion Fund which co-finances mainly transport and environmental projects.
The ERDF supports regions covered by all three objectives. In relation to convergence, it focuses intervention on modernising and diversifying economic structures, as well as safeguarding or creating sustainable jobs. As regards regional competitiveness and employment, its priorities relate to innovation and the knowledge-based economy, environment and risk prevention, and access to transport and telecommunications services of general economic interest. Finally, in terms of its contribution to European territorial cooperation, the ERDF aims to develop economic and social cross-border activities, the establishment and development of transnational cooperation, and to increase the efficiency of regional policy through interregional promotion and cooperation, as well as the networking and exchange of experiences between regional and local authorities.
The ESF aims to improve employment and job opportunities through interventions that are made within the framework of convergence and regional competitiveness and employment objectives. The ESF supports actions in four key areas: increasing the adaptability of workers and enterprises (lifelong learning, designing and spreading innovative working organisations); enhancing access to employment and participation in labour markets; reinforcing social inclusion by combating discrimination and facilitating access to labour markets among disadvantaged people; and promoting partnership for reform in the fields of employment and inclusion.
The Cohesion Fund supports actions within the framework of the convergence objective; it finances activities including trans-European transport network and environmental projects, as well as energy or transport projects, as long as these demonstrate environmental benefits (such as energy efficiency, the use of renewable energy, developing rail transport systems or improving public transport); this fund concerns Bulgaria, the Czech Republic, Estonia, Greece, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Portugal, Romania, Slovenia and Slovakia; while Spain is eligible to a phase-out fund.
One particular focus of economic and social cohesion policy has been urban development. Europe’s cities are centres of economic activity, attracting innovation and employment. Upwards of 70 % of the EU’s population live in urban areas, yet a considerable proportion face problems such as crime, poverty, unemployment, housing, traffic or environmental pressures. The URBAN I Community initiative ran from 1994 to 1999 covering 118 urban areas, with projects focused on the rehabilitation of infrastructure, job creation, combating social exclusion and environmental improvements. URBAN II ran from 2000 to 2006 supporting development and regeneration strategies in 70 urban areas. As of 2007, the EU has reinforced the urban dimension of regional policy and fully integrated this into cohesion policy, with particular attention to promoting social cohesion and environmental sustainability. As such, the guiding principles of the URBAN Community Initiative have been incorporated into operational programmes; this change means that all cities are potential beneficiaries of funding, through support for different sectoral and thematic policies in the context of the revised Lisbon Strategy, the Sustainable Development Strategy and other EU priorities (for example, urban regeneration, urban transport, the rehabilitation of industrial sites and contaminated land areas, or housing developments).
Further Eurostat information
- Eurostat regional yearbook 2009 (available in English, French and German)
- Regions and cities, see:
- Main tables
- Regional statistics (t_reg)
- Regional demographic statistics (t_reg_dem)
- Regional economic accounts - ESA95 (t_reg_eco)
- Regional education statistics (t_reg_educ)
- Regional labour market statistics (t_reg_lmk)
- Regional science and technology statistics (t_reg_sct)
- Regional information society statistics (t_reg_isoc)
- Regional tourism statistics (t_reg_tour)
- Regional agriculture statistics (t_reg_agr)
- Regional health statistics (t_reg_health)
- Regional transport statistics (t_reg_trans)
- Regional statistics (t_reg)
- Main tables
- Regions and cities, see:
- Regional statistics (reg)
- Regional agriculture statistics (reg_agr)
- Regional demographic statistics (reg_dem)
- Regional economic accounts - ESA95 (reg_eco)
- Regional education statistics (reg_educ)
- Regional science and technology statistics (reg_sct)
- Regional structural business statistics (reg_sbs)
- Regional health statistics (reg_hlth)
- Regional tourism statistics (reg_tour)
- Regional transport statistics (reg_tran)
- Regional labour market statistics (reg_lmk)
- Regional labour costs statistics (reg_lcs)
- Regional information society statistics (reg_isoc)
- Regional migration statistics (reg_mig)
- Regional statistics (reg)
Methodology / Metadata
- Regional education statistics (ESMS metadata file - reg_educ_esms)
- Regional employment - LFS series (ESMS metadata file - reg_lfemp_esms)
- Regional information society statistics (ESMS metadata file - isoc_reg_esms)
- Regional migration statistics (ESMS metadata file - reg_mig_esms)
- Regional structural business statistics (ESMS metadata file - reg_sbs_esms)
- Regional transport statistics (ESMS metadata file - reg_tran_esms)
- Regulation 1059/2003 of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS)